Alarmist Algorithms: Why Pricing Bots Won’t Be the End of Society

Federal Trade Commission Acting Chairman Ohlhausen and Commissioner McSweeny recently gave two very different speeches on algorithmic pricing. Commissioner McSweeny’s speech seemed to express concern that algorithms would lead to price fixing, coordination and higher prices. The Chairman seemed less concerned. The Chairman is likely correct. Pricing bots are not a new, sinister specter haunting society like the trusts of the late 19th Century. Nor are they boogeymen hiding under your bed waiting to raise price and siphon off what little money you have left after rent, clothing and food. Pricing bots are digitized, more efficient versions of the same thought processes that any rival engages in to set price. Because they are digital, they are not constrained by volume. They can take into account significantly more variables than the human mind is incapable of keeping track of—and do it much faster. Using a bot to set price unilaterally is not an antitrust problem. It is the essence of the free market. By contrast, sharing your algorithms, your pricing formulae, with competitors can very well be a problem because it can lead to collusive behavior, like any other exchange of competitively sensitive business information. Setting up encrypted, digital communications systems to suggest and agree on price increases and market allocations is easier today, but it’s still illegal. And it’s sufficiently difficult to do now that a trove of evidence to prove its existence and illegality will be available.

Generally, it’s important to remember that if a market is otherwise competitive, a pricing bot cannot make it less competitive. If a firm can raise price after implementing a pricing bot, it means that the firm was not maximizing profits because it lacked sufficient information. In short, in the pre-bot days, the costs of additional information outweighed the expected benefits of additional information. The bot reduces the cost of additional information. The fact that it raised prices (or lowered them) does not tell us anything about the state of competition in the market.

Pricing bots cannot be coded to “agree” to raise price absent the intent of the human coders. Coders must specifically design their systems to seek out competitors, determine collusive prices and police those prices. Given the highly disparate systems and price setting methodologies that exist across even a commoditized market, the amount of time and effort needed to create such a system is vast. Extrinsic evidence of those efforts must exist. That will include all of the normal evidence of collusion. For a more thorough discussion of what exactly a price-fixing bot might look at, please see our discussion on price fixing bots.

The rest of this brief addresses specific issues raised in each of the speeches.

Algorithms may make price fixing attempts more frequent and potentially more difficult to detect.

Price fixing conspiracies are unnecessary in concentrated markets. Parties can simply act in parallel. Price fixing conspiracies do not work in atomistic markets because identifying and disciplining cheaters is too difficult. Somewhere in the middle are markets that have just enough participants to make parallel behavior difficult. For example, there may not be an efficient way for everyone to follow the behavior of others. What a bot can do in those markets is eliminate the problems with the communications and policing that make parallel behavior difficult. In this regard, a bot could increase the maximum number of participants that can successfully act in parallel because it makes the process more efficient.

The more harmonized pricing is, the more likely the product is a commodity. The more likely the product is a commodity, the more likely the market will have too many participants to act in parallel or collude. A price fixing bot will therefore only work in markets with a small number of participants. How a given firm prices can be highly idiosyncratic. Most firms in such markets will not share the same inventory and sales software. Creating a piece of software that will set price uniformly across a group that is sufficiently small enough to collude is going to require a lot of work by a lot of people. The more individuals aware of a price fixing conspiracy, the more likely evidence the conspiracy exists.

A price fixing bot comes up with a supracompetitive price the conspirators can charge for their product. It is basically the digital version of the smoke-filled room. Knowing how the conspirators arrived at a collusive price is not necessary to prosecute a price fixing claim. You don’t need to know what the code of the bot is to establish that the parties who coded it were fixing price. And given the number of people needed to create a price fixing bot, you are going to have a lot more evidence if they are creating and relying on a bot than if they had just sat down and decided on a price in that smoke-filled room. In this vein, a conspiracy implemented and policed through software is going to leave a much bigger footprint than one accomplished the traditional way.

Here, the Commissioner’s assertion is inaccurate. Coding an industry-wide price fixing bot is time consuming, reads too many people into the scheme, leaves a mammoth paper trail, and ultimately will have little effect on the ability of an end-user to figure out that it’s paying more for the product than it would otherwise. Bots do not necessarily make price fixing attempts more frequent and potentially more difficult to detect.

True, but only in a very narrow number of markets. Coordinated behavior is only possible in concentrated markets. If a market is atomistic, a price increase by one or several participants will only result in immediate diversion to discounters. Being able to see a rival’s prices quicker and more broadly will not change that fundamental dynamic. In a concentrated market, parties may act in parallel because there isn’t a sufficient number of participants to constrain their activity. A bot won’t change that situation either. There may be some markets where there are just enough participants to make parallel behavior difficult. That may be because there is insufficient ability to detect and police behavior. A bot, combined with a good system of announcing prices to the market, could make it easier for participants to act in parallel in those markets where they couldn’t before. Here, though, all the bot is doing is making detection and policing more efficient. The market still must be sufficiently concentrated for the parties to act in parallel. As such, the bot is no more inherently illegal than any other method of disseminating and reacting to information.

In analyzing whether a price increase is potentially a consequence of illegal collusion facilitated by a bot, one must exclude the possibility that the sellers lacked sufficient information about the price sensitivity of their customers to charge the appropriate price. It is entirely possible that a particular product could have significant value to an as-yet unidentified group that is willing to pay more for that product. The bot could very well have facilitated the discovery of that smaller discrimination market. Pricing more to that group does not reduce consumer welfare and is not an antitrust issue.

A pricing bot could be used to signal prices to other competitors by sending them potential price increases before they are presented to the public. This is Airline Tariff Publishing, however. The parties would be using the bots as a means of proposing and responding to target prices. This is price fixing; it is not parallel behavior.

It is inaccurate to broadly condemn bots. One must examine the facts of any given market to assess the effects of a bot. Only in concentrated markets where the bot is intentionally designed to set a common price among participants is it illegal. And then, due to the complexity of creating such a bot, there will be plenty of evidence.

Multiple competitors might use algorithmic pricing software offered by the same company.

This is a hub and spoke conspiracy where the manufacturer of the software has coded it to set prices uniformly for all members of a market. Again, however, such a program will only work in a concentrated market. If a program raised price in an atomistic market, customers would divert to the discounter. If everyone used the same bot—an improbable hypothetical—at least one participant would see an opportunity to capture sales and turn off the bot. Again, the bot may make oligopolistic pricing easier because it makes policing easier, but at some point, there will simply be too many participants for such a scheme to work.

This only works in concentrated markets where customers cannot readily switch to punish a price increase. As such, the sellers in this market must not be pricing optimally; there must be an informational asymmetry of which customers were taking advantage. It is also possible that the algorithms have made seller information more transparent such that the maximum number of sellers capable of behaving in parallel is increased.

Pricing algorithms can be “an effective tool for tacit collusion” with the potential to lead to near-monopolistic pricing.

True. The use of the words “collusion” and “near-monopolistic pricing” suggest that this pricing algorithm can be illegal. For the reasons stated above, that is not accurate.

The model assumes that firms are able to “decode” their competitors’ algorithms. [The author] included a specification in which firms were given an option to mask their algorithms to prevent decoding. The firms in the model chose not to exercise the option…

An algorithm is essentially a mathematical or logical function. Price is the output of that function. One can display that price as a static number. One need not display the terms of the function to display the price. The only way displaying such a formula would be profitable to a company is if the market were sufficiently concentrated that its rivals could use the formula to increase their own prices. If the algorithms were made available only to rivals, one could argue this was an illegal information exchange—as it would be if the parties shared their actual cost structures with rivals in any other media. The concern with the information exchange is less if the information is also shared with customers. But presumably the only condition in which a seller would share with both is if the consumer had insufficient choice to defeat the potential price increases.

It is therefore incorrect to conclude pricing bots will necessarily lead to “near-monopolistic pricing” in all markets. They can facilitate parallel behavior in near-concentrated markets by making the observation of and reaction to rival activity easier.

I do not think you could draw even this conclusion from this paper, however. There is a difference in the output of the algorithm and the terms of the algorithm. The former is a number; the latter is the method by which a firm arrives at the number. By making the content of the algorithm discoverable, the author is in effect permitting the parties to engage in price fixing. The information exchange is so thorough that the parties have no doubt as to the behavior of rivals and can act in concert. His conclusion is therefore a tautology: markets which are sufficiently concentrated that price fixing can work will produce higher prices if the parties are allowed to engage in price fixing.

One gas station operator candidly told the Journal that is decision to use the software was promoted by the effects of a years-long price war with its competitors. . . . [W]ithout more information, it’s hard to know whether the reported higher margins are the result of coordinated effects.

A merger that results in a market becoming sufficiently concentrated, such that the participants can behave in a coordinated fashion where they could not before, can potentially violate Section 7 of the Clayton Act, which prohibits mergers or acquisitions that substantially lessen competition or create a monopoly. Unilateral conduct that improves understanding of how a market operates is not a violation of Section 7. Nor is it a violation of Section 2.

It is entirely possible that the algorithms the gas station operator deployed “knew” its customers a lot better than the operator did. The software could know, for example, that consumers that purchase gas at 700a on a Monday are likely on their way to work, need gas to get there and don’t really care how much they have to pay to get that gas. Consumers who purchase gas at 1100a on a Saturday likely do care what they are paying. It is perfectly acceptable to charge the Monday morning commuter more for gas because he values that gas more.

The third concern with pricing algorithms is that they may enable price discrimination strategies that lead to higher prices for certain groups of customers.

This is the essence of big data. It enables sellers to discover ever smaller price discrimination markets. Charging a person more because his utility for the product is higher is neither inefficient nor an antitrust violation.

It works just as well for customers who care very much, but are nonetheless willing to pay a higher price because they lack the practical ability to go elsewhere.

If a customer “lacks the practical ability to go elsewhere,” the subject product has a narrow geographic market and that market is concentrated. The bot has nothing to do with that fact.

Pricing algorithms will undoubtedly lead to an increase in price discrimination. Whether that is a good or a bad thing for consumers is likely to depend on facts that are specific to individual markets and individual algorithms.

No, it’s economic efficiency. Consumers that would pay more but do not are free riders: they are exploiting an informational asymmetry between seller and buyer. In this case, it benefits a buyer. But that does not mean eliminating that inefficiency is harmful to society.

If algorithms enable firms to “solve” their unique prisoner’s dilemmas without resorting to overt collusion, that would be great news for them but bad news for consumers.

The purpose of the antitrust laws is to create a prisoner’s dilemma: they prohibit communication that would otherwise allow rivals to set supracomeptitive prices to consumers. Bots do not necessarily create new, undetectable and unassailable communications flows between competitors. What they can do, in very limited circumstances, is enable sellers to realize that there are categories of customers that will pay more for their products. In essence, they can eliminate an informational asymmetry that allows those consumers to free ride off the ignorance of a seller. The antitrust laws were not designed to foster this type of free riding. Indeed, this reading of the antitrust laws would cause them to lock in seller-side inefficiencies.

This statement assumes all algorithms are the same. It is important to understand what the “algorithm” is doing. If sellers are sharing the model by which they arrive at a price with their competitors, they are engaging in a potentially illegal information exchange. If sellers have eliminated inefficient pricing to consumers who are willing to pay more through the use of bots, the sellers are engaging in perfectly legal, and efficient, unilateral behavior. If a group of sellers have harmonized their pricing software to take input from competitors and respond to it by raising price, those sellers have automated an illegal price fixing scheme and are potentially guilty of a criminal violation of Section 1. If your market is sufficiently concentrated such that you can set price based on a competitors’ price, and you create a bot that will scan that publicly available price and set your price accordingly, you are automating parallel behavior which is not illegal under the antitrust laws.

[I]t would be helpful to understand whether algorithms are resulting in coordinated effects and, if so, under what conditions.

Pricing bots will not affect concentrated or unconcentrated markets. There may be a small class of markets that could behave in a concentrated fashion that does not because the technology is not sufficient for the actors to detect and police price movements. In that instance, a bot could make a difference. But the bot is no more inherently illegal than any other technology that makes it more efficient for people to communicate. A merger that creates a concentrated market can violate Section 7. A market where participants can act in parallel because the technology is better is not a violation of Section 7 or Section 1.

Sharing the basis for one’s pricing decisions with ones competitors, or creating competitor information exchanges that allow competitors to agree on a common price, and police those prices, can be price fixing.

An algorithm can include a virtually unlimited number of rules, conditions and variables. This means that many extremely complex and nuanced behaviors can be modeled in a set of detailed computer instructions.

Correct.

It is axiomatic that we cannot tell firms to ignore the public behavior or their rivals when they set prices without deleting the “free” in free market.

Correct.

[W]e try to make sure, primarily through our merger enforcement program, that the conditions that allow this kind of behavior to take place generally do not arise in the first place. We also prohibit explicit agreements to set prices collusively and exchanges of competitively sensitive, non-public information between competitors.

The Salcedo study basically asks whether rivals would prefer to engage in an exchange of competitively sensitive, non-public information or guess what their rivals’ behavior was. If they are rational, they will always pick the information exchange; that’s why there are laws limiting the exchange of such information and ultimately whey price fixing is illegal.

For example, when the products are highly differentiated, or the market participants have different cost structures, or transactions are relatively infrequent, it is very difficult to maintain stable, interdependent pricing just by watching the behavior of your rivals.

This is another way of saying pricing and cost structures are highly idiosyncratic. Creating an algorithm that would be able to interface with several disparate systems would require the heavy and active participation of many, many individuals and firms. One of the reasons the B2B craze died out so abruptly was that harmonizing all of those back office systems so that they could all communicate with each other—for legitimate commercial purposes—was exceedingly difficult. Enforcers looking at potential collusion in larger industrial markets may want to see if there are any of these residual ‘e-marketplaces’ functioning and whether there are price harmonization components to them. In that particular instance, the e-marketplace joint venture could be facilitating coordinated behavior. If the e-marketplace were independent of the participants, you’d look at it in terms of being a hub of a conspiracy.

What I’d like to suggest to you this evening is that this same analytical framework is sufficiently flexible and robust that it can already accommodate several of the current concerns applicable to the widespread use of algorithms.

Correct. You do have to figure out what people mean when they say “algorithm.” For the most part, it is simply a digitization of the process by which a firm sets price. All the firm is doing is taking what was their “brain-based” decision making on price and turning it into a computer program that can take into account many, many more variables and calculate responses much more quickly. After many years selling gas, the man who takes the ladder and changes his price may very well know, instinctively, that he can raise price by 10 cents a gallon at the tail end of a holiday weekend. A computer can reach that same conclusion much more quickly. It’s not witchcraft.

[T]he algorithms are programmed to produce some sort of signal to the market, a signal that only the other market participants, similarly armed with algorithms of their own, will be able to detect.

Creating a program that is able to understand communications from an alien program, and then set price according to those signals, is a remarkably difficult task. ATP involved using the electronic fare system more as a bulletin board where human beings could see price jumps and drops and react to them than a program that could react to those changes and set price. A cartel created by people could very well code a communications system that could easily send secret messages to each other regarding price. This is just the modern equivalent to allocating markets based on the phase of the moon. These are just communications vehicles. They do not exist in a vacuum; human beings have communicated with each other about creating and using them. There will be extrinsic evidence beyond the encrypted communications tools that demonstrate the existence of a conspiracy. Nor is a case against these malfeasants harder because the communications are better hidden. A prosecutor does not need to explain how a telephone can convert speech into 1s and 0s and back to establish someone used a phone to commit a crime.

It is important to distinguish between publishing price to the market, where rivals can see and react to those prices, and publishing the algorithms (the formulae) that determine what those prices are. The former is not a problem; the latter can be aclassic illegal information exchange.

[T]he firms themselves don’t directly share their pricing strategies, but that information still ends up in common hands, and that shared information is then used to maximize market-wide prices.

Price will go up as a consequence of a bot only if (1) the bot sets a collusive price; or (2) the market is sufficiently concentrated but the ability of participants to observe and react to rivals’ behavior is technologically limited and the bot corrects that. The Agencies must eliminate the possibility of (2) before they can conclude a price increase is a consequence of a collusive bot. If it’s not (1), then the market is behaving like any other concentrated market exposed to new and better technology.

Stay Connected

About the Antitrust and Competition practice Group

Kelley Drye's Antitrust and Competition attorneys guide companies, trade groups and individuals through this intricate legal terrain that can determine success or failure in today's competitive global marketplace. Our clients benefit from the deep experience of our group in each of the major areas of antitrust and competition law and consumer protection: transactions, government investigations, civil litigation, and counseling and compliance.